The book begins with an introduction to the language of statistics and then covers descriptive statistics and inferential statistics. Throughout, the author offers readers:- Difficulty Rating Index for each chapter′s material- Tips for doing and thinking about a statistical technique- Top tens for everything from the best ways to create a graph to the most effective techniques for data collection- Steps that break techniques down into a clear sequence of procedures- SPSS tips for executing each major statistical technique- Practice exercises at the end of each chapter, followed by worked out solutions.The book concludes with a statistical software sampler and a description of the best Internet sites for statistical information and data resources. Readers also have access to a website for downloading data that they can use to practice additional exercises from the book. Students and researchers will appreciate the book′s unhurried pace and thorough, friendly presentation.

    Readers profit from its verbal-conceptual exposition and frequent use of examples.The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 .Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

    Friedman, the Pulitzer Prize-winning foreign affairs columnist for The New York Times, offers an engrossing look at the new international system that is transforming world affairs today. Globalization has replaced the Cold War system with the integration of capital, technology, and information across national borders—uniting Brazilian peasants, Indonesian entrepreneurs, Chinese villagers, and Silicon Valley technocrats in a single global village. You cannot understand the morning news, know where to invest your money, or think about the future unless you understand this new system, which is profoundly influencing virtually every country in the world today. Friedman tells you what this electronic global economy is all about and what it will take to live within it.With vivid stories drawn from his extensive travels, he dramatizes the conflict of “the Lexus and the olive tree”—the tension between the globalization system and the ancient forces of culture, geography, tradition, and community. He also details the powerful backlash that globalization produces among those who feel brutalized by it, and he spells out what we all need to do to keep the Lexus and the olive tree in balance. For this new paperback edition, Friedman has substantially expanded and updated his provocative analysis, making it essential reading for all who care about how the world works now.

    With boundless erudition and in delightfully clear prose, Surowiecki ranges across fields as diverse as popular culture, psychology, ant biology, behavioral economics, artificial intelligence, military history, and politics to show how this simple idea offers important lessons for how we live our lives, select our leaders, run our companies, and think about our world.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    With millions of copies sold, the Publication Manual of the American Psychological Association is the style manual of choice for writers, editors, students, educators, and professionals in psychology, sociology, business, economics, nursing, social work, and justice administration, and other disciplines in which effective communication with words and data is fundamental.In addition to providing clear guidance on grammar, the mechanics of writing, and APA style, the Publication Manual offers an authoritative and easy-to-use reference and citation system and comprehensive coverage of the treatment of numbers, metrication, statistical and mathematical data, tables, and figures for use in writing, reports, or presentations. The new edition has been revised and updated to include: The latest guidelines and examples for referencing electronic and online sources New and revised guidelines for submitting papers electronically Improved guidelines for avoiding plagiarism Simplified formatting guidelines for writers using up-to-date word-processing software All new guidelines for presenting case studies Improved guidelines for the construction of tables Updates on copyright and permissions issues for writers New reference examples for audiovisual media and patents An expanded and improved index for quick and easy access Writers, scholars, and professionals will also find: New guidelines on how to choose text, tables, or figures to present data Guidelines for writing cover letters for submitting articles for publication, plus a sample letter Expanded guidelines on the retention of raw data New advice on establishing written agreements for the use of shared data New information on the responsibilities of co-authors New and experienced readers alike will find the 5th Edition a complete resource for writing, presenting, or publishing with clarity and persuasiveness.Approximately 400 pages

    Even the simplest numbers can become powerful forces when manipulated by politicians or the media, but in the case of the law, your liberty -- and your life -- can depend on the right calculation. In Math on Trial, mathematicians Leila Schneps and Coralie Colmez describe ten trials spanning from the nineteenth century to today, in which mathematical arguments were used -- and disastrously misused -- as evidence. They tell the stories of Sally Clark, who was accused of murdering her children by a doctor with a faulty sense of calculation; of nineteenth-century tycoon Hetty Green, whose dispute over her aunt's will became a signal case in the forensic use of mathematics; and of the case of Amanda Knox, in which a judge's misunderstanding of probability led him to discount critical evidence -- which might have kept her in jail. Offering a fresh angle on cases from the nineteenth-century Dreyfus affair to the murder trial of Dutch nurse Lucia de Berk, Schneps and Colmez show how the improper application of mathematical concepts can mean the difference between walking free and life in prison. A colorful narrative of mathematical abuse, Math on Trial blends courtroom drama, history, and math to show that legal expertise isn't't always enough to prove a person innocent.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    The text progresses from a micro- to macro-level of analysis, focusing first on such problems as illness and health care, drugs and alcohol, and family problems and then broadening to the larger issues of poverty and inequality, population growth, environmental problems, and conflict around the world. The social problem in each chapter is framed in a global as well as U.S. context. In every chapter, the three major theoretical perspectives are applied to the social problem under discussion, and the consequences of the problem, as well as alternative solutions, are explored. Pedagogical features such as The Human Side and Self and Society enable students to grasp how social problems affect the lives of individuals and apply their understanding of social problems to their own lives.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    To illustrate the various theories, and to reveal their implications, important current issues such as abortion, euthanasia, the treatment of nonhuman animals, racial discrimination, and nuclear weapons are discussed in depth.

    Based in part on Dr. Ruby K. Payne's myth shattering A Framework for Understanding Poverty, Bridges reaches out to the millions of service providers and businesses whose daily work connects them with the lives of people in poverty. In a highly readable format you'll find case studies, detailed analysis, helpful charts and exercises, and specific solutions you and your organization can implement right now to: Redesign programs to better serve people you work with; build skill sets for management to help guide employees; upgrade training for front-line staff like receptionists, case workers, and managers; improve treatment outcomes in health care and behavioral health care; increase the liklihood of moving from welfare to work. If your business, agency, or organization works with people from poverty, only a deeper understanding of their challenges--and strengths--will help you partner with them to create opportunities for success.

    While the mathematics is there, the effort is not all concentrated on proofs. Strang's emphasis is on understanding. He explains concepts, rather than deduces. This book is written in an informal and personal style and teaches real mathematics. The gears change in Chapter 2 as students reach the introduction of vector spaces. Throughout the book, the theory is motivated and reinforced by genuine applications, allowing pure mathematicians to teach applied mathematics.

    First published before the World Trade Organization protests in Seattle, this is an infuriating, inspiring, and altogether pioneering work of cultural criticism that investigates money, marketing, and the anti-corporate movement.As global corporations compete for the hearts and wallets of consumers who not only buy their products but willingly advertise them from head to toe—witness today's schoolbooks, superstores, sporting arenas, and brand-name synergy—a new generation has begun to battle consumerism with its own best weapons. In this provocative, well-written study, a front-line report on that battle, we learn how the Nike swoosh has changed from an athletic status-symbol to a metaphor for sweatshop labor, how teenaged McDonald's workers are risking their jobs to join the Teamsters, and how "culture jammers" utilize spray paint, computer-hacking acumen, and anti-propagandist wordplay to undercut the slogans and meanings of billboard ads (as in "Joe Chemo" for "Joe Camel").No Logo will challenge and enlighten students of sociology, economics, popular culture, international affairs, and marketing."This book is not another account of the power of the select group of corporate Goliaths that have gathered to form our de facto global government. Rather, it is an attempt to analyze and document the forces opposing corporate rule, and to lay out the particular set of cultural and economic conditions that made the emergence of that opposition inevitable." —Naomi Klein, from her Introduction